# SEC Form 144 Insider Selling Monitor - Planned Sell Signals (`datasignalslab/sec-form144-insider-selling-monitor`) Actor SEC Form 144 insider selling monitor: planned insider sales with seller, role and dollar value from filings, scored. Pay per company. - **URL**: https://apify.com/datasignalslab/sec-form144-insider-selling-monitor.md - **Developed by:** [DataSignals Lab](https://apify.com/datasignalslab) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $200.00 / 1,000 company analyzeds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## SEC Form 144 Insider Selling Monitor: Planned Insider Sales, Scored **See which insiders are filing to sell, and how much, before the sale happens.** This Actor turns SEC Form 144 filings into a structured **insider selling** feed: give it one or more companies and it returns the recent planned insider sales per company, with the named seller, their relationship to the issuer, and the proposed dollar value, parsed from official SEC EDGAR data and scored by impact. It is built as a leading-indicator companion to Form 4 insider buying, so you can watch distribution and accumulation side by side. ### Why this is different Most EDGAR tools hand you raw filing rows and leave the work to you. This **insider sell monitor** reads the structured Form 144 XML and builds the signal layer on top: - **Structured seller, role, and value.** Every notice carries the named insider, their relationship to the issuer (officer, director, affiliate, or 10 percent owner), the securities class, the units, and the aggregate market value of the planned sale, pulled directly from the filing XML. - **Scored, not just listed.** Each notice gets a transparent impact score from 0 to 100 based on proposed dollar size, seller role, and recency, so the largest and most recent planned sales rank first. - **Per-company aggregate.** You get a total proposed-sell value and a high-impact count across all recent notices for each company, not just a flat list. - **A companion to Form 4 buying.** Form 144 is a notice of intent to sell restricted or control stock and tends to precede the executed sale that later appears on Form 4. Pairing planned sales (this Actor) with executed buys (Form 4) gives you both sides of insider flow. - **Verifiable.** Every notice links straight back to its filing on SEC.gov. ### Who it's for - **Traders and quants** who want a ready-made insider-selling feed to screen watchlists and feed models, complementary to insider buying. - **Fintech and data apps** that need clean, structured planned-sale signals to drop into a product or dashboard. - **Research and risk teams** tracking insider distribution around earnings, lock-up expirations, and price highs. ### Use cases - **Watchlist distribution scan.** Run your watchlist daily and surface companies where insiders are filing large, recent planned sales. - **Pair with insider buying.** Cross-reference Form 144 planned sells against Form 4 executed buys to see where insiders are accumulating versus distributing. - **Event-driven monitoring.** Schedule a run around earnings or lock-up expiration to catch a cluster of officer and director sell notices. - **Risk overlay.** Flag positions where a high-impact insider sell notice has just been filed and route it into your risk review. - **Feed for models and agents.** Pull scored planned-sale data on demand into a quant pipeline, notebook, or AI agent workflow. ### Input | Field | Type | Required | Description | |---|---|---|---| | `tickers` | array | yes | One or more US stock tickers (e.g. TSLA, NVDA, AAPL) or 10-digit SEC CIK numbers. Each company is scanned for recent Form 144 insider-sale notices. | | `maxFilings` | integer | no | How many of the company's most recent Form 144 notices to parse and rank. Default 25, range 1 to 60. | | `minImpact` | integer | no | Only return notices at or above this impact score, 0 to 100. For example 70 returns large or recent planned sells. Default 0 returns all. | ### Output The Actor pushes one item per company, with the ticker, company name, notices found, total proposed-sell value, a high-impact count, and a ranked list of notices. Each notice includes the seller, relationship, securities class, units, aggregate market value, approximate sale date, exchange, filing date, a plain-language catalyst description, the impact score, the accession number, and a direct SEC.gov URL. ```json { "type": "insider_sell_notices", "ticker": "TSLA", "company": "Tesla, Inc.", "notices_found": 12, "total_proposed_sell_value": 26034000.0, "high_impact_count": 2, "notices": [ { "issuer": "Tesla, Inc.", "ticker": "TSLA", "seller": "Kimbal Musk", "relationship": "Director", "securities_class": "Common Stock", "units": 75000, "aggregate_value": 24980000.0, "approx_sale_date": "2026-06-02", "exchange": "NASDAQ", "filing_date": "2026-06-01", "catalyst": "Kimbal Musk (Director) filed to sell $24,980,000 of Common Stock, approx 2026-06-02", "impact": 100, "accession": "0000000000-26-000000", "url": "https://www.sec.gov/Archives/edgar/data/1318605/000000000026000000/" } ] } ```` The example values above illustrate the shape and field types. Live values come straight from the filings. ### How the score works The impact score is transparent and computed directly in code: 1. **Proposed dollar size** sets the base. A planned sale of 10M or more starts at 90, 1M or more at 70, 100k or more at 50, any disclosed value above zero at 35, and an undisclosed value at 25. 2. **Seller role** adds a bonus. If the relationship contains officer or director, add 5, because executives and directors tend to weigh more than passive large holders. 3. **Recency** adds a bonus. A filing within 14 days adds 12, and within 45 days adds 6. The total is capped at 100. Notices are ranked by impact first, then by aggregate value, so a large, recent, officer-filed planned sale rises to the top. ### Use with AI agents and automation This Actor is built to be called by code and by AI agents, not just clicked in the UI. - **AI frameworks.** Call it from LangChain or LlamaIndex as a tool inside an agent pipeline. - **Apify MCP server.** Expose it over the Apify MCP server so assistants such as Claude, ChatGPT, and Cursor can invoke it directly and read back the scored insider-selling data. - **No-code automation.** Wire it into Zapier or Make to trigger downstream steps when new high-impact notices appear. - **Webhooks and schedules.** Run it on demand for fresh JSON, or on a schedule, and use Apify webhooks to push results into your own systems when a run finishes. ### Pricing **Pay per event: one charge per company analyzed.** There is no subscription. Scanning a 10-ticker watchlist is 10 charges, so you pay only for the companies you scan. ### Data source and compliance Data comes from the official **SEC EDGAR submissions API** at data.sec.gov, which is free, public, and requires no API key. The Actor maps each ticker to its CIK, filters for Form 144 filings, then reads each filing's structured primary-document XML to extract the seller, relationship, and proposed dollar value. This is public regulatory data, and the Actor surfaces only what the filing itself discloses. It does not collect or add personal data beyond the filer information published in the filing. ### FAQ **What is a Form 144?** It is a notice of intent. An insider files Form 144 to declare a proposed sale of restricted or control securities. It signals that a sale is planned, ahead of the actual transaction, which later appears on Form 4. **Does a Form 144 mean the shares were sold?** No. It is a notice of intent to sell, not a confirmation that the sale occurred. Treat it as a leading indicator. **Can an AI agent call this automatically?** Yes. Expose the Actor through the Apify MCP server and assistants like Claude, ChatGPT, and Cursor can call it on their own, or invoke it from LangChain or LlamaIndex inside an agent workflow. **How current is the data?** SEC EDGAR is the official US filing registry and is updated continuously. Each run reads the latest filings available at that moment. **How does it relate to insider buying?** Form 144 captures planned insider selling, while Form 4 captures executed insider transactions including buys. Use them together to see both distribution and accumulation. **Can I filter out small notices?** Yes. Set `minImpact` to return only notices at or above a given score, for example 70 for large or recent planned sells. *** *Keywords: insider selling, SEC Form 144, planned insider sales, insider sell monitor, insider sell data, proposed sale, restricted stock, SEC EDGAR API, insider transactions, Form 4 companion, executive stock sales, insider distribution.* *** **Note:** This Actor is provided as data for research, screening, and monitoring. It is not investment advice. Historical patterns do not guarantee future results. # Actor input Schema ## `tickers` (type: `array`): One or more US stock tickers (e.g. TSLA, NVDA, AAPL) or 10-digit SEC CIK numbers. Each is scanned for recent Form 144 insider-sale notices. ## `maxFilings` (type: `integer`): How many of the company's most recent Form 144 notices to parse and rank. ## `minImpact` (type: `integer`): Only return notices at or above this impact score (e.g. 70 = large/recent planned sells). 0 = all. ## Actor input object example ```json { "tickers": [ "TSLA", "NVDA" ], "maxFilings": 25, "minImpact": 0 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "tickers": [ "TSLA", "NVDA" ] }; // Run the Actor and wait for it to finish const run = await client.actor("datasignalslab/sec-form144-insider-selling-monitor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "tickers": [ "TSLA", "NVDA", ] } # Run the Actor and wait for it to finish run = client.actor("datasignalslab/sec-form144-insider-selling-monitor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "tickers": [ "TSLA", "NVDA" ] }' | apify call datasignalslab/sec-form144-insider-selling-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datasignalslab/sec-form144-insider-selling-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SEC Form 144 Insider Selling Monitor - Planned Sell Signals", "description": "SEC Form 144 insider selling monitor: planned insider sales with seller, role and dollar value from filings, scored. Pay per company.", "version": "0.1", "x-build-id": "b54pMHgIDtFBAszeo" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datasignalslab~sec-form144-insider-selling-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datasignalslab-sec-form144-insider-selling-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datasignalslab~sec-form144-insider-selling-monitor/runs": { "post": { "operationId": "runs-sync-datasignalslab-sec-form144-insider-selling-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datasignalslab~sec-form144-insider-selling-monitor/run-sync": { "post": { "operationId": "run-sync-datasignalslab-sec-form144-insider-selling-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "tickers" ], "properties": { "tickers": { "title": "Tickers or CIKs", "type": "array", "description": "One or more US stock tickers (e.g. TSLA, NVDA, AAPL) or 10-digit SEC CIK numbers. Each is scanned for recent Form 144 insider-sale notices.", "items": { "type": "string" } }, "maxFilings": { "title": "Max filings per company", "minimum": 1, "maximum": 60, "type": "integer", "description": "How many of the company's most recent Form 144 notices to parse and rank.", "default": 25 }, "minImpact": { "title": "Minimum impact score (0-100)", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only return notices at or above this impact score (e.g. 70 = large/recent planned sells). 0 = all.", "default": 0 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```